MEMS陀螺的抗野值自适应滤波降噪方法

针对微机电系统(MEMS)陀螺随机漂移较大及量测信息中野值对滤波的不利影响,提出了一种抗野值自适应滤波降噪方法。该方法采用Allan方差信息估计量测噪声方差参数,避免了Kalman滤波器与量测噪声估值器之间的相互关联,能有效抑制滤波发散。在此基础上引入新息抗野值算法,通过修正新息去除野值的不利影响,增强对随机漂移的滤波效果。实测数据试验结果表明,采用该文方法滤波后的MEMS陀螺输出信号均方差及角度随机游走都比滤波前明显降低,验证了提出的滤波方法在MEMS陀螺降噪中的有效性。

De noising Method of Outlier Rejecting and Adaptive Filtering for MEMS Gyroscope

Aimed at the problem of large random drift of micro electro mechanical systems (MEMS) gyroscope and the negative impact of filter due to the outliers in measurement, an outlier rejecting and adaptive filtering method is proposed for MEMS de noising. The Allan variance is used to estimated the parameters of the measurement noise variance, thus the connection between Kalman filter and estimator of measurement noise is avoided and the filter divergence is suppressed effectively. On this basis, the outlier rejecting algorithm of new information is introduced. The negative influence of outliers is removed by revising the new information and the de noising effect is enhanced. The experimental result from the practical gyro measurement show that the mean square error and angle random walk have been reduced significantly by using the proposed method, which prove that the proposed filtering method is effective for de noising of MEMS gyroscope.